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Found 6 papers, 0 papers with code
Learn Dynamic-Aware State Embedding for Transfer Learning
In this setting, the MDP dynamic is a good knowledge to transfer, which can be inferred by uniformly random policy.
Differentiable Linear Bandit Algorithm
Theoretically, we show that the proposed algorithm achieves a $\tilde{\mathcal{O}}(\hat{\beta}\sqrt{dT})$ upper bound of $T$-round regret, where $d$ is the dimension of arm features and $\hat{\beta}$ is the learned size of confidence bound.
Laplacian-regularized graph bandits: Algorithms and theoretical analysis
In terms of network regret (sum of cumulative regret over $n$ users), the proposed algorithm leads to a scaling as $\tilde{\mathcal{O}}(\Psi d\sqrt{nT})$, which is a significant improvement over $\tilde{\mathcal{O}}(nd\sqrt{T})$ in the state-of-the-art algorithm \algo{Gob. Lin} \Ccite{cesa2013gang}.
Error Analysis on Graph Laplacian Regularized Estimator
We provide a theoretical analysis of the representation learning problem aimed at learning the latent variables (design matrix) $\Theta$ of observations $Y$ with the knowledge of the coefficient matrix $X$.
Data Driven Chiller Plant Energy Optimization with Domain Knowledge
Refrigeration and chiller optimization is an important and well studied topic in mechanical engineering, mostly taking advantage of physical models, designed on top of over-simplified assumptions, over the equipments.
Graph-Based Recommendation System
In this work, we study recommendation systems modelled as contextual multi-armed bandit (MAB) problems.
